Background description includes information that may be useful in understanding the present invention. It is not an admission that any of the information provided herein is prior art or relevant to the presently claimed invention, or that any publication specifically or implicitly referenced is prior art.
Magnetic resonance imaging (“MRI”) is a well-known, highly useful technique for diagnosing abnormalities in biological tissues. MRI can detect abnormalities that are difficult or impossible to detect by other techniques, without the use of x-rays or invasive procedures. Also, MRI is widely used technique for grading tumors, especially for malignant gliomas (brain tumors) due to their inherent inaccessibility.
However, till date, conventional MRI has not been capable enough to distinguish accurately between normal, benign, and malignant tissues. This is primarily because tissues have a number of distinguishing characteristics, which change for each patient and the tissue being monitored, and therefore a fixed threshold for classifying the tissue is not possible.
Magnetic Resonance Imaging (MRI) uses multiple quantitative and qualitative parameters and attributes that are evaluated and measured pre and post injection of the contrast for determining and classifying the tissue/lesion under investigation. Apparent diffusion coefficient (ADC) is one such quantitative parameter, which measures magnitude of diffusion (of water molecules) within tissue. A low ADC value indicates higher compactness of cells in a unit area and indicates towards malignancy, whereas high ADC, on the other hand, indicates less compactness of cells in a unit area and indicates towards benignity. The objective of using ADC as a parameter is to determine water diffusion in tissue region, wherein a lower value of ADC indicates decrease in inter cellular space as seen in cancerous tissues. Diffusion MRI is also used in evaluating effectiveness of treatment by monitoring water diffusion values for the tissue region. Diffusion MRI can be used to assess treatment effect through quantification of the amount of increased apparent diffusion coefficient (ADC) values in tumor regions experiencing a loss of cellular density. However, ADC is sensitive to changes in tissue microstructure and depends on a number of variable attributes such as “b values”, which make the ADC estimate unreliable and noise sensitive.
Magnetic Resonance Spectroscopy (MRS) is also commonly used for non-invasive examination of metabolic characteristics of human cancers in a clinical environment. Accessible nuclei include 31P, 13C, 1H, and 23Na. 31P MRS contains information about energy status (phosphocreatine, inorganic phosphate, and nucleoside triphosphates), phospholipids metabolites (phosphomonoesters and phosphodiesters), intracellular pH (pH NMR), and free cellular magnesium concentration (Mg2+f). Water-suppressed 1H MRS, a frequently used technique, shows total choline, total creatine, NAA (N-Acetyl L-Aspartate), lipids, glutamate, inositols, lactate, and the like. Choline/Creatine, Choline/NAA (N-Acetyl Aspartate), Lipid and Lactate ratios are a commonly used parameters used as biomarker to classify tissues. Negendank, W., NMR in Biomedicine, 5, 303-324 (1992). (Harish 1995 AJNR ref—AJNR Am J Neuroradiol 16:1593-1603, September 1995.
Positron Emission Tomography (“PET”) is an imaging technology that depicts distribution of radiotracers that get accumulated in a tissue (uptake) proportionate to metabolism and tissue function. The device can provide body tissue related molecular and functional information in very high contrast. PET tracers such as flurodeoxyglucose (FDG) and FluoroEthyleTyrosineare used to depict higher uptake in cancer tissue and lower uptake in benign lesions.
However, since a PET device fundamentally has a low resolution, there is a limitation in providing anatomical information. In contrast, an MRI device can provide detailed anatomical information about body tissues, but has a limitation in providing molecular and functional information when compared with a PET device.
Dynamic susceptibility perfusion imaging is a MRI technique, which is based on dynamic contrast enhancement (DCE) and is widely used for grading tumors, especially for gliomas. Perfusion imaging of tumors is becoming increasingly important due to its usefulness to demonstrate vascular growth (angiogenesis and neovascularization) associated with tumor growth by imaging the Blood Volume (BV) or Blood Flow (BF) in a tumor.
Blood volume map (BV)or Blood flow (BF) maps provide volume of blood in a region of tissue. The blood volume can be used to evaluate micro-vascular density or vascularity, in other words, density of small blood vessels (capillaries) in a tissue region. Perfusion imaging whereby images are acquired before, during and after injection of a contrast agent and BV values are calculated to correlate with the grade of vascularity; high-grade (malign) tumors tend to have higher BV values than low-grade (less malign) tumors. In practice high and low grade gliomas based on relative cerebral blood volume (rCBV) maps are obtained by perfusion MRI. A general way to characterize glioma malignancy is by measuring the ratio between the most elevated rCBV area within the glioma (“hot-spot”), and an unaffected contra-lateral white matter rCBV value. Although several notations are used, this ratio is often referred to as normalized CBV (nCBV), and high-grade gliomas tend to have a higher nCBV ratio than low-grade gliomas. Perfusion imaging is therefore helpful in the grading of tumors. However, due to relatively small voxel sizes (typically tens of mm2) of the perfusion imaging technique, large vessels in the region could result in a misleading shift of the BV frequency distribution towards higher BV values. Hence, it is necessary to develop an improved method to quantify and validate errors involved in calculating the voxel size from dynamic perfusion imaging technique, improve accuracy of correct classification/categorization/characterization of malignant and non-malignant tissues.
With the advent of simultaneous PET-MRI, it is possible to obtain voxel wise multiparametric information from all the MR based parameters like ADC from Diffusion images, nCBV or/and nCBF from perfusion images, Choline/Creatine from proton MR Spectroscopy and SUV from PET in a single examination. The present disclosure envisages to develop a time efficient, reliable and reproducible diagnostic technique and tool for voxel wise analysis of clustered parameters on individual weighing towards tissue characterization derived from MRI and PET for characterization of tissues based on parametric mapping.
The present invention satisfies these needs, as well as others, and efficiently overcomes the deficiencies found in the background art.